Self-Distillation with no labels (DINO)  involves harvesting regions of interest without class-level labels. To achieve this goal, the authors trained two networks, called student and teacher, to match their probability distribution. Parameters of the teacher are an exponential moving average of the student network. The probability \(p\)  of the representation for these networks is calculated by normalizing the output of the concerned network using the softmax function with temperature defined in Eq. REF  (\(\tau \)  is a hyper-parameter to be optimized). Furthermore, the input \(x\)  is augmented in different ways, \(x_1\)  and \(x_2\) , for the student and teacher networks, respectively. They represent two set of distorted views of image \(x\) , calculated using the strategy defined in [1]}. The first set contains several local views, while the second one contains two global views. Here, local views contain less than \(50\%\)  of the original image, and global views cover more than \(50\%\)  of the area. Finally, the loss is calculated as cross-entropy between the output probability of the student \(p_s\)  and the teacher \(p_t\) :
\(\min _{\theta _s} -p_s(x_1) \log ( p_t(x_2) ). \) 
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